journal of language, technology & entrepreneurship …

JOURNAL OF LANGUAGE, TECHNOLOGY & ENTREPRENEURSHIP IN AFRICA Volume 12, No.1, 2021

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EFFECT OF BUDGETING PRACTICES ON FINANCIAL PERFORMANCE OF

MANUFACTURING SMALL AND MEDIUM ENTERPRISES IN NAIROBI

COUNTY, KENYA

Marion Mbogo

Chandaria School of Business

United States International University-Africa

Email: [email protected]

Clement Olando

School of Business and Economics

Mount Kenya University


Jimmy Macharia, PhD

School of Science and Technology

United States International University-Africa


ABSTRACT

Prior studies have asserted that small and medium-sized enterprises (SMEs) have grown and

represented most businesses in Kenya. However, these studies continue to establish that 70%

of Small-to-Medium sized enterprises (SMEs) in Kenya fail within their first three years of

existence. One weakness postulated as a possible cause for this failure rate is poor financial

performance. Existing literature has highlighted management accounting practices

deployment, including budgeting, costing, and strategic management accounting practices.

This is one possible remedy from an array of interventions. This paper, therefore, aims to

investigate the effect of budgeting practices, including planning for cash flows (BP),

controlling cash flows (BC), resources allocation (BRA), activity coordination (AC), and

monitoring financial position (MFP) on Financial Performance (FPM) of Manufacturing Small

and Medium Enterprises in Nairobi County, Kenya. This research adopted a descriptive

research design that used data collected using a self-administered cross-sectional survey. A

questionnaire from a randomly selected sample of 156 manufacturing SMEs in the City of

Nairobi data was analyzed through structural equation modelling. The results revealed that

budgeting practices positively and significantly influence manufacturing SME's financial

performance. The findings of this study suggest that the financial performance of a

manufacturing SME can be improved by deploying strategic action in budgeting practices in

the form of planning for cash flows (BP), controlling cash flows (BC), resources allocation

(BRA), activity coordination (AC) and monitoring financial position (MFP).

.

Keywords: Budgeting Practices, Planning for Cash flows (BP), Controlling Cash flows (BC),

Resources Allocation (BRA), Activity Coordination (AC), and Monitoring Financial Position

(MFP)

mailto:[email protected]




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1. INTRODUCTION

The International Federation of Accountants (IFAC) (1998) shows that management

accounting evolved through four stages. Stage one was prior to 1950, where the focus was

mainly on the analysis of the financial statement, ratio analysis, and budgeting. Ahmad (2017),

in a study in Malaysian SMEs, stated that the use of traditional management accounting

practices (MAPs) like costing, budgeting, and performance management systems (PMS) was

greater than for more advanced MAPs such as strategic management accounting (SMA). The

use of MAPs in manufacturing companies is mostly for the provision of information for

decision-making followed by strategic analysis, budgeting, performance evaluation, and

costing, among others. According to Gichaaga (2014), some measures of performance, such

as Return on Equity (ROE) and Return on Assets (ROA), are found to have increased as a result

of the application of MAPs.

King, Clarkson, and Wallace (2010) carried out research to determine the relationship between

budgeting and firm performance in small healthcare businesses in Australia. The study

objectives were to investigate the relationship between contextual factors identified from

contingency-based research, the adoption and extent of use of budgets, and business

performance within the Australian primary healthcare setting. Unlike their study that was in

healthcare and in the developed world, the focus of the current paper is manufacturing SMEs

in the context of a developing nation.

2. PROBLEM STATEMENT

Small and medium enterprises in Kenya indicate high rates of business failure. Although there

are a number of support programmes for SMEs provided by the government and other players

in the sector, the high failure rate is still persistent, indicating poor financial performance.

According to Douglas et al. (2017), 70% of Small-to-Medium sized enterprises (SMEs) in

Kenya fail within their first three years of existence. Moreover, the problem of SME failure

rate is widespread across many nations of the earth. For example, in the UK, 50% of business

start-ups fail within five years (Douglas et al., 2017). In South Africa, SMEs failure rate is

projected to be between 70% and 80%, with a 70% failure rate within the first year of operation

(Rabie, Cant, & Wiid, 2016). Despite this high failure rate, governments all over the world

have continued to underscore the importance of SMEs and place high expectations in their

contribution to the Gross Domestic Product(GDP), job and wealth creation, economic

development, and stability by employing and engaging the huge numbers of unemployed

youths particularly in developing Nations(Douglas et al., 2017; Dalberg, 2011). Consequently,

governments, development partners, and sector experts have been grappling with this challenge

of high SME failure rate. A myriad of interventions and support programs have been proposed

and tried with varying degree of success, including training (Rabie et al., 2016), and tax

incentives (Yoshino & Taghizadeh-Hesary, 2016), budgeting practices (Adu-Gyamfi,

Yusheng, & Chipwere, 2020), cost accounting practices (Kariyawasam, 2018), strategic

management accounting practices (Okoye & Akenbor, 2012) among others. Unfortunately, the

problem of the high SME failure rate is still persistent. Since the manufacturing SMEs have

the largest contribution for opportunities for job and wealth creation, as well as contribution to

GDP, its financial performance and consequent success has the most probable effect on a

national economy. Thus, this study chooses to investigate the effect of Budgeting Practices on

the Financial Performance of Manufacturing Small and Medium Enterprises in Nairobi County,

Kenya.


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Abbadi, (2013) asserts that their results indicate a lack of using budgeting practices in the

developing countries and points out improvement which would take place in terms of the

adoption of more advanced budgeting practices. However, the Abbadi, (2013) study focused

on financial institutions in Jordan. The current study focuses on manufacturing SMEs in Kenya.

Moreover, the measures of central tendency are used in the analysis of their variables, while

the current study used structural equation modelling to investigate the study variables namely

Planning for Cash flows (BP), Controlling Cash flows (BC), Resources Allocation (BRA),

Activity Coordination (AC) and Monitoring Financial Position (MFP). Likewise, the study by

Adu-Gyamfi et al. (2020) study in Ghana differs from the current study in that they focused on

organizational performance as opposed to financial performance, used regression analysis, and

examined a number of other variables that are not measurements of budgeting practices which

is the primary focus of this paper. Consequently, the paper focuses on the Effect of Budgeting

Practices on Financial Performance of Manufacturing Small and Medium Enterprises in

Nairobi County, Kenya.

3. LITERATURE REVIEW

3.1 Theoretical Foundation of Contingency Theory

Contingency is defined as any variable that regulates the effect of firm characteristics on firm

performance and presumes that different circumstances require different solutions and different

organizational structures (Dobák & Antal, 2010) cited in Kihara (2016). The theory postulates

that there is no best way of organizing, leading, directing, or making decisions in a company

but that firms take the ideal course of action depending on their existing internal and external

circumstances (Abba, Yahaya, & Suleiman, 2018). Additionally, Abba, et al (2018) state that

the adoption of contingency theory in accounting resulted from conflicting research results that

could not adequately be resolved within a universal framework. Therefore, in the MA context,

the contingency theory approach assumes that there is no universally acceptable accounting

information system that fits all organizations in all circumstances. According to its

requirements, each organization applies its own unique MAPs (Ajibolade, 2013; Otley, 2016a).

Principally, every organization implements its own management accounting practices.

3.2 Empirical Literature Review

A budget is a detailed estimate of future transactions which are articulated in terms of human

resources, physical quantities, money, or all (Kang’aru & Tirimba, 2018). The principle of a

budget is that it is a goal established for management to operate within, accomplish or exceed

it. In general, the underpinning principle for budgeted financial statements is detail budgets.

Detail budgets comprise of production forecasts, sales forecasts, and other approximations in

support of the financial proposal. According to Zwikael and Sadeh, (2007), a budget includes

financial planning and indicates the essential cash flow for each time period. They argue that

consistent budget plan review ought to focus more on the role level rather than the activity

level.

3.2.1 Budgetary Planning

Nair (2020) and Agbenyo et al. (2018) define budgetary planning as the process of estimating

future events and how activities should be handled based on predetermined targets set by the

firm. Kibunja (2017) study on the budgetary process and financial performance of Murang'a

county government in Kenya used a sample of 83 staff. The study established that budgetary

planning, implementation, monitoring, and evaluation had a significant influence on the


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financial performance of the county government. Wijewardena and De Zoysa (2001) in Yang,

(2010) investigated the impact of financial planning and control on the performance of SMEs

in Australia and argue that the impact of budgetary control and budget planning on performance

may differ from company to company subject to the degree of its use. In their study, two

measures of financial performance indicators are return on investment and sales growth. The

study measures financial planning in four items, namely perceptions on return on investment

(Net income divided by total investments), return on assets (Net income divided by total

assets), the percentage change in operating profit before tax, and percentage change in net profit

margin. This study collected data from 2,000 manufacturing SMEs in Australia. The findings

reveal a positive and significant influence of budget planning and budgetary control on sales

growth. The study by Siyanbola, (2013) investigating the impact of budgeting and budgetary

control on the performance of manufacturing companies in Nigeria established that there is a

significant relationship between budgetary planning and firm performance. Likewise, Nair,

(2020) studied 200 SME business owners in Yemen and affirmed a significant relationship

between budgetary control, budgetary planning, and SME financial performance in Yemen.

However, in Nair (2020) study the focus was not on manufacturing SMEs. The study by

Mbuthia and Omagwa (2019) on budgetary control established that budget planning had the

most significant effect on selected commercial banks' financial performance in Kenya,

followed by budget implementation, budget review, and budget control. However, their study

was on commercial banks in Kenya as opposed to manufacturing SMEs. Through this review,

it is hypothesized that:

Hypothesis 1: there is a significant positive relationship between budgetary planning, and the

financial performance of Manufacturing SMEs in Nairobi County, Kenya.

3.2.2 Budgetary Control

Myint (2019) defines budgetary control as the procedure of developing a disbursement plan

and occasionally linking actual spending against that budget to control whether spending

behavioural patterns need to be regulated accordingly. Koech, 2015) studied the effect of

budgetary control on the financial performance of manufacturing firms in Kenya, where one

of their findings was that budgetary control determines budgetary skills and financial skills to

make better decisions. Further, Koech (2015) identifies how and when to track the financial

metrics for the firm which aid in understanding budgets and performance indicators as

communication tools. However, the performance was general as opposed to financial

performance, which is the focus of this study. The study by Mbuthia and Omagwa, (2019) on

the effect of budgetary control on the financial performance of selected commercial banks in

Kenya established that budget control had a positive and significant effect on financial

performance. However, their study was on commercial banks in Kenya as opposed to

Manufacturing SMEs. A study on Effect of budget and budgetary control on firms

performance: a case study of the East African Portland Cement Company Limited, concluded

that there was a high positive correlation of 54.4% between budgetary control and firm's

financial performance measured in terms of profit before (Nafisatu, 2018). However, the study

sample was 45, and it was based on a case study of one manufacturing company. The study by

Siyanbola, 2013) posted a significant relationship between budgetary control and firm

performance in Nigeria.. Thus, it is hypothesized that:

Hypothesis 2: there is a significant positive relationship between budget control and the



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3.2.3 Resource allocation

According to Green et al. (2000), resource allocation could be defined as the general allocation

of financial resources to devolved management units or departments within the government,

an organization or a company. It is closely linked to budgeting, which focuses on statements

of specific spending plans within this general allocative upper limit. Economic Value Added

(EVA) measures whether the operating profit is sufficient enough to cover the cost of capital.

EVA measurement also requires a company to be more careful about resource mobilization,

resource allocation, and investment decisions (Malik, 2013). Additionally, EVA effectively

measures the productivity of all aspects of production. Atsmon et al. (2016), in their book

titled "Resource allocation: Selected articles from the Strategy and Corporate Finance

Practice," posts that due to the richness and complexity of the resource allocation issues,

variances in the relationship between long and short-term resource allocations and financial

performance is likely to be a fruitful area for further research. Thus, it is hypothesized that:

Hypothesis 3: there is a significant positive relationship between resource allocation and the


3.2.4 Activity coordination

Romenti and Illia (2013) define activity coordination as the continuous alignment among

corporate values and daily collective behaviours. Zhu et al. (2012) argue that the coordination

structure for allocation of organizational resources to handle complex tasks of activity

coordination is necessary for enhancing efficiency and environmental performance gains.

Activity coordination supports firm performance, together with access to further resources for

research and development (R&D)(Lundberg & Andresen, 2012). In the study by Hara, (2020)

it is argued that activity coordination is a central issue in the activity-link dimension and that

among firms' activities coordination is assisted by inter-firm interaction and information

sharing. Prior studies posit that every activity of the internal functions of the firm should be

regarded as a value-adding activity. Additionally, coordination of these activities plays a major

role in bringing the value-added services to the end-user (Hussain, Shah, & Akhtar, 2016).

Budgets support the coordination of all ranges of activity, section units and division's activities.

This is because they integrate a plan that drives the firm toward attaining the set goal

(Klimaitienė & Ramanauskaitė, 2019). Generally, the activity coordination, control, and

direction of service and material flows through end-to-end steps that are executed according to

managerial supervision (Kimpimäki, 2014). In today's world of greater digitization, electronic

hierarchies will continue evolving to facilitate integrated activity coordination mechanisms and

processes across organizational borders by permitting uninterrupted sharing of information

effortlessly using online platforms and systems.(Kimpimäki, 2014). Thus, it is hypothesized

that:

Hypothesis 4: there is a significant positive relationship between activity coordination and the


3.2.5 Monitoring financial position

The results of the study by Mandela, (2014) on the effect of budgetary control process on firm

financial performance: a case study of Nzoia Sugar Company, Kenya, indicated that there was

no significant relationship between the budget monitoring and the financial performance.

According to The World Bank Group (2007), budget execution encompasses both activities

related to the implementation of policies and tasks related to the administration of the budget.


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In governments, the ministry of finance should have the responsibilities of administering the

system of release of funds, control of budget execution, preparing the in-year financial plan,

and preparing in-year budget revisions. Additionally, it should monitor expenditure flow;

managing the central payment system (if any); administering the central payroll system (if any);

or supervising government bank accounts, and preparing accounts and financial reports (The

World Bank Group, 2007). Moreover, there is scanty published literature on the influence of

monitoring financial position or budget monitoring on firm’s financial performance.

Consequently, it is hypothesized that:

Hypothesis 5: there is a significant positive relationship between monitoring financial

position/ budget monitoring and the financial performance of Manufacturing SMEs in Nairobi

County, Kenya.

3.2.6 Financial Performance

Financial performance measures are quantitative performance measures calculated from the

financial statements and are highly accepted because the information is readily available from

a firm's financial statements (Ahmad, 2012). The measures include return on equity (ROE),

return on assets (ROA), return on investments (ROI), sales volume, profitability, market share,

firm reputation, and established corporate identity (Taticchi, Tonelli, & Cagnazzo, 2010).

These performance measures are applicable to and mostly used by large companies but are not

always appropriate for SMEs. Although extensive research has been carried out on financial

performance measurement systems in large organizations, available research relating to SMEs

is low (Anggadwita & Mustafid, 2014). Thus, it is hypothesized that:

Hypothesis 6: there is a significant positive relationship between budgeting practices and the


3.2.7 Conceptual Framework

Sekaran & Bougie (2016) refer to a conceptual framework as a written description or schematic

diagram that helps the reader to visualize the relationships between the theorized variables.

Figure 1 shows the conceptual framework with the independent variables, Planning for Cash

flows (BP), Controlling Cash flows (BC), Allocating Resources (BRA), Coordinating

Activities (AC), Monitoring Financial Position (MFP); endogenous variable Budgeting

Practices (B_P), and the dependent variable Financial performance (FPM).


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Figure 1: Conceptual Model

(Researcher 2021)

4. METHODOLOGY

4.1 Research Design

Sekaran & Bougie (2016) state that research philosophy is a belief about the way in which data

on a phenomenon should be gathered, analyzed and used. This paper adopted a positivist

research philosophy since its data was quantitative. A research design is an arrangement of how

data will be effectively and efficiently collected and analyzed and in a manner that is relevant

to address the research questions (Kothari, 2014). The study adopted a descriptive research

design using a cross-sectional survey.

4.2 Data collection

The minimum sample was considered using an online calculator for structural equation

modelling by Soper (2021). A self-administered survey questionnaire was given to a

representative sample of 254 manufacturing small and medium enterprises (SME), that yielded

156 usable responses. A disproportionate stratified random sampling procedure was employed.

4.3 Measures

The measures of this research were adapted from prior studies with modifications to fit the

specific context of the manufacturing SME environments. Measurements for independent

variables, Planning for Cash flows (BP), Controlling Cash flows (BC), Allocating Resources

(BRA), Coordinating Activities (AC), Monitoring Financial Position (MFP and the dependent

variable Financial Performance (FPM) were phrased on a five-point Likert scale, from 1 =

strongly disagree to 5 = strongly agree.

4.4 Data Analysis

In the analysis of the data, both psychometric properties and model testing were assessed

through Structural Equation Modelling (SEM) using R-Statistics software and to test the study

hypotheses. R statistics is one of the most widely used structural equation modelling (SEM)


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techniques. Chin, (1998), posits that if SEM is precisely applied, it can surpass such first-

generation techniques as principal components analysis, factor analysis, discriminant analysis,

or multiple regression. This is because it provides superior flexibility in estimating associations

among many predictors and criterion variables and permits modelling with unobservable latent

variables. Further, it makes assessments of the model uncontaminated with measurement

errors (Lee, Cheung, & Chen, 2005).

4.5 Reliability, Validity, and Fit Indices

4.5.1 Reliability

Generally, reliability is the degree of how reliable is the study measurement model in

measuring the envisioned underlying constructs (Munir, 2018). The reliability of the

measurement model is assessed based on the criteria detailed in Table 1. Prior research has

revealed that there are three benchmarks for the assessment of reliability for a measurement

model:

Table 1. Reliability Measures

Reliability Criteria

Internal reliability Internal reliability is achieved when the Cronbach's Alpha value is

0.6 or higher (Ahmad et al., 2016)

Composite reliability/

Construct reliability

The measure of reliability and internal consistency of the measured

variables representing a latent construct. To achieve the construct

reliability also known as composite reliability, a value of CR ≥ 0.6

is required (Ahmad et al., 2016).

Average variance

Extracted

Average Variance Extracted (AVE) is the average percentage of

variation explained by the items in a construct. An AVE ≥ 0.5 is

required (Ahmad et al., 2016).

The formula to calculate the value of Construct Reliability (CR) and Average Variance

Extracted (AVE) are shown in Table 2 below.

Table 2. The formula for CR and AVE

Formula Notes

CR (Ʃκ)² / [(Ʃκ)² + (Ʃ1 - κ²)]

AVE Ʃ κ² / n

K = factor loading of every item n = number of

items in a model

4.5.2 Validity

Validity is the ability of an instrument to measure what is supposed to be measured for a

construct (Zainudin Awang, 2015). The validity of the measurement model is assessed based

on the requirements stated in Table 3. There are three types of validity required for each

measurement model:


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Table 3. Validity Measures

Validity Requirements

Convergent validity The convergent validity is achieved when all items in a

measurement model are statistically significant. This validity

could also be verified through Average Variance Extracted

(AVE). The value of AVE should be greater or equal to 0.5 to

achieve this validity

Construct validity The construct validity is achieved when the Fitness Indexes

achieve the level of acceptance.

Discriminant validity The discriminant validity is achieved when the measurement

model is free from redundant items. Another requirement for

discriminant validity is that correlation between each pair of

the latent exogenous construct should be less than 0.85. Other

than that, the square root of AVE for the construct should be

higher than the correlation between the respective constructs

(Zainudin Awang, 2015)

4.5.3 Fit Indices

The data was analyzed by Structural Equation Modelling (SEM) using AMOS 23.0 software.

SEM is a multivariate technique, which estimates a series of inter-related dependence

relationships simultaneously. The hypothesized model can be tested statistically in

simultaneous analysis of the entire system of variables to determine the extent to which it is

consistent with the data (Ahmad et al., 2016). There are several Fitness Indices in SEM that

reflect how fit the model is to the data. The use of at least one fitness index from each category

of model fit is recommended (Awang, 2015). The information concerning the model fit

category, their level of acceptance, and literature are presented in Table 4.

Table 4. Fitness indices Measures

Name of

category

Name of

index

Index name Level of

acceptance

Literature

Absolute Fit Chisq Discrepancy chi square p ≤ 0.05 (Wheaton, 1987)

RMSEA Root Mean Square of

Error Approximation

≤ 0.08 (Browne & Cudeck,

1992)

GFI The goodness of Fit Index ≥ 0.90 (Jöreskog, Olsson, & Y.

Wallentin, 2016)

Incremental Fit AGFI Adjusted Goodness of Fit ≤ 0.90 (Tanaka & Huba, 1985)

CFI Comparative Fit Index ≥ 0.90 (Bentler & Hu, 1998)

TLI Tucker-Lewis Index ≥ 0.90 (Bentler & Hu, 1998)

NFI Normed Fit Index ≥0.90 (Bollen, 1989)

Parsimonious

Fit

Chisq/df Chi Square/Degree of

freedom

≤ 5.0 (Marsh & Hocevar,

1985)


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5. RESULTS: PATH ANALYSIS

5.1 Descriptive Analysis for Budgeting Practices

The respondents comprised of 63% Males and 4% females. The age distribution was as

follows: 22-29 years-22.4%, 30-39 years 53.2%, 40 -49 years- 19.2%, and 50 years and

above- 5.2%. Their respondents' position and education in the firm is indicated in Table 5.

Table 5: Position and Education Status of Respondents

Variable Labels Frequency Percent

Position Owner 4 2.6

Partner 6 3.8

Manager 76 48.7

Accountant 70 44.9

Education High School 11 7.1

Bachelor’s Degree 85 54.5

Diploma 29 18.6

Masters/Doctorate 30 19.2

Source: Researcher (2020)

The respondents who were managers in 12 manufacturing sectors are shown in Figure 2.


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Figure 2: Manufacturing SME Sector Distribution among Respondents

The study sought to establish the use of budgeting practices and their influence on the financial

performance of SMEs. This was done by comparing the means of the variables describing the

budgeting practices dimensions. The respondents were asked to respond to items testing their

level of agreement with statements on a scale of 1 to 5 where 1 represented strongly disagree

and 5 represented strongly agree. The data were analysed using descriptive statistics of mean

and standard deviation. The standard deviation indicated the consensus of the respondents.

Variables with a mean of 4.0 or higher represented "strongly agree." A mean score close to 3.0

represented "Neutral," and a mean of 2.0 and below represented disagree and strongly disagree.

Table 6 shows findings of descriptive analysis for budgeting practices.

Table 6: Descriptive Statistics Scores for Budgeting practices

N Minimum Maximum Mean Std.

Deviation

Variance

Statistic Statistic Statistic Statistic Std. Error Statistic Statistic

BP 240 2 5 4.46 0.041 0.633 0.401

BC 240 1 5 4.23 0.05 0.776 0.602

BRA 240 2.25 5 4.37 0.035 0.541 0.292

AC 240 1 5 4.08 0.046 0.715 0.512

MFP 240 1.67 5 4.42 0.039 0.599 0.359

FPM 240 1 5 3.76 0.061 0.951 0.905


Table 6 shows that all the variables mean scores of higher than 3 with budget planning having

the highest mean score of 4.46 out of the possible 5. The lowest mean score was Financial

Performances, with a mean score of 3.76 out of a possible 5. This shows that a majority of the

respondents strongly agreed that budget planning was the top practice in the manufacturing

SMEs but activity coordination was the lowest. The standard deviation for budget planning

was SD=0.633, meaning that the data for budget planning was mostly concentrated around the

mean. Controlling Cash flows had the highest standard deviation of 0.776.

5.2 Validity and Reliability

5.2.1 Reliability for Budgeting Practices

Table 7 shows that the model using the constructs of BP-Budget Planning, BC-Controlling

Cash flows, BRA-Budget Resource Allocation, AC-Coordinating Activities, MFP-Monitoring

Financial Position, and the dependent variable, FPM-Financial performance all met the

reliability criteria. All were above the cut-off rate of 0.7 as suggested by Sekaran and Bougie

(2016) and Hair et al. (2014).


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Table 7: Budgeting Practices Reliability Results Constructs for Budgeting

Practices

Code Loadings SMC Cronbach's

Alpha

CR AVE

Planning for Cash flows

(BP)

BP2 0.752 0.566

BP3 0.808 0.653 0.676 0.757 0.735

Controlling Cash flows

(BC)

BC2 0.744 0.554

BC3 0.684 0.468 0.840 0.676 0.641

Resources Allocation

(BRA)

BRA1 0.728 0.530

BRA2 0.731 0.534

BRA3 0.757 0.573

BRA4 0.757 0.573 0.810 0.832 0.683

Activity Coordination

(AC)

AC2 0.515 0.265

AC3 0.703 0.494

AC4 0.576 0.332 0.823 0.628 0.475

Monitoring Financial

Position (MFP)

MFP2 0.779 0.607

MFP3 0.797 0.635

MFP4 0.754 0.569 0.844 0.846 0.730

Firm Performance

(FRM)

FPM1 0.865 0.748

FPM2 0.886 0.785

FPM3 0.859 0.738 0.880 0.903 0.853


5.2.2 Validity for Budgeting Practices

Validity in this study was measured by examining construct validity (Markus, 2012) and using

Average Variance Extracted (AVE). Table 8 shows that the validity of the model for budgeting

practices using the three constructs of BP, BC, BRA, AC and MFP as well as the dependent

variable, FPM all met the validity criteria for budgeting practices as in each case the AVE value

is greater than 0.5 except for BP and AC and SQRT (AVE) is greater than all the correlation in

that row or column (Hair et al., 2014).

Table 8: Discriminant Validity Results for Budgeting Practices

Variable AVE SQRT(AVE) BP BC BRA AC MFP FPM

BP 0.482 0.694 1

BC 0.634 0.796 .326** 1

BRA 0.610 0.781 .432** .471** 1

AC 0.493 0.702 .430** .648** .553** 1

MFP 0.686 0.828 .270** .573** .430** .582** 1

FPM 0.795 0.891 s.235** .255** .344** .355** .296** 1


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**. Correlation is significant at the 0.01 level (2-tailed). .

*Correlation is significant at the 0.05 level (2-tailed).


5.3 Diagnostic testing

5.3.1 Exploratory Factor Analysis for Budgeting practices

Budgeting practices were hypothesized as a second-order latent construct identified by the five

first-order latent variables: budget planning, budget control, resource allocation, activity

coordination, and financial position monitoring. Factor analysis was carried out in order to

reduce the measurement items for budget practices and develop appropriate measures for

Kaiser-Meyer-Olkin (KMO) and Bartlett test of sphericity as well as total variance explained

by the components. Table 9 indicates that KMO measure of sampling adequacy resulted in

0.8, which is greater than 0.5 as recommended. This suggested that the data was suitable for

factor analysis with a data set of the number of observations and the variables. The Bartlett's

test of sphericity was significant (χ2 (7, N=156) = 52.608, p < 0.00), also suggesting that

correlation patterns are close and that factor analysis would yield reliable factors.

Table 9: Kaiser-Meyer-Olkin (KMO) and Bartlett's Test of Sphericity for Budgeting

Practices.

Kaiser-Meyer-Olkin measure of Sampling Adequacy 0.8

Bartlett’s Test of Sphericity Approx. Chi-Square 52.608

Df 7

Sig. 0.000


5.3.2 Total Variance Explained for Budgeting practices

To determine the number of factors that represent the interrelations among the budgeting

practices measuring constructs, this study employed variance percentage (Hair et al., 2014).

Based on the five factors (Planning for Cash flows, Controlling Cash flows, Resources

Allocation, Coordinating Activities, and Monitoring Financial Position) were computed and

each had a loading (eigenvalues) greater than 1. These six factors explained 68.286 percent of

the total variance in the variations of budgeting practices, as indicated in Table 10.


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Table 10: Total Variance Explained for Budgeting Practices

Total Variance Explained

Component

Initial Eigenvalues

Extraction Sums of

Squared Loadings

Rotation Sums of Squared

Loadings

Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 5.777 33.981 33.981 5.777 33.981 33.981 2.688 15.812 15.812

2 1.917 11.274 45.254 1.917 11.274 45.254 2.634 15.492 31.304

3 1.646 9.681 54.935 1.646 9.681 54.935 2.339 13.757 45.061

4 1.236 7.268 62.203 1.236 7.268 62.203 2.301 13.533 58.594

5 1.034 6.083 68.286 1.034 6.083 68.286 1.648 9.692 68.286

6 .936 5.506 73.792

7 .862 5.073 78.864

8 .629 3.700 82.564

9 .532 3.131 85.696

10 .504 2.963 88.658

11 .469 2.758 91.416

12 .335 1.972 93.388

13 .281 1.656 95.044

14 .260 1.532 96.576

15 .225 1.325 97.901

16 .196 1.153 99.053

17 .161 .947 100.000

Extraction Method: Principal Component Analysis.


5.3.3 Pattern Matrix Coefficients

Budgeting practices in this study consisted of five components which included budget

planning, budget control, resource allocation, activity coordination, and monitoring financial

position. However, factor analysis results eliminated some latent variables in budget control,

resource allocation, and monitoring financial position. The following measuring items did not

load, BP1, BP4, BC1, BC4, AC1, and MFP1. Consequently, these were dropped from further

analysis. The study evaluated goodness of fit using both absolute and incremental fit indices.

The validity check of this measurement model indicated there was a satisfactory level of model

fit. In this study, the pattern matrix coefficients for budget practices after factor analysis ranged

from 0.515 to 0.886, thus showing that the variables were well related to a factor pattern, as

indicated in Table 11.


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Table 11: Pattern Matrix for Budgeting Practices

Rotated Component Matrix

Items

Component

1 2 3 4

BP2 0.752

BP3 0.808

BC2 0.744

BC3 0.684

BRA1 0.728

BRA2 0.731

BRA3 0.757

BRA4 0.757

AC2 0.515

AC3 0.703

AC4 0.576

MFP2 0.779

MFP3 0.797

MFP4 0.754

FPM1 0.865

FPM2 0.886

FPM3 0.859


5.4 Measurement Model

Confirmatory factor analysis (CFA) was conducted to assess the extent to which the data fit

the pre-specified theoretically-driven model. CFA is usually employed to confirm a priori

hypothesis about the relationship between a set of measurement items and their respective

factors. The CFA results for budgeting practices construct show that the Chi-square value was

160.104 with 72 degrees of freedom. The p-value associated with this result was significant at

p=0.000. In addition to the χ2 result, the value for CFI, an incremental fit index, was 0.947,

which is above the 0.90 thresholds (Hair et al., 2014) hence acceptable. The values for absolute

fit indices were 0.918 for goodness-of-fit (GFI), which is above the required 0.90 thresholds

and therefore acceptable (Hair et al., 2014) and 0.071 for RMSEA. These results suggest that

the measurement model for budgeting practices provided a reasonably good fit.


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99

Chi-square (χ2) = 160.104, DF = 72, P-VALUE = 0.000, CMIN/DF (x2 /df =2.224,

RMSEA = 0.071, IFI= 0.947, CFI= 0.946, NFI= 0.907, GFI =0.912, AGFI=0.834

Figure 3: Model Fit for Budgeting Practices after Confirmatory Factor Analysis

Figure 3 indicates that the factor loading estimates were significant and ideal (above 0.30 at

p=0.00). An examination of inter-correlations between the three dimensions of budgeting

practices showed all estimates to be ranging from 0.6 to 0.9, implying discriminant validity.

There were no cross-loadings among the measured variables. These results supported the

measurement model validity, and hypothesis one was confirmed, which stated that budgeting

practices as a second-order latent construct composed of budget planning, budget control,

budget resource allocation, activity coordination and monitoring financial position.

Table 12: R- Square Values for Budgeting Practice as Dependent Variable

Variable R-square Variable R-square

BP2 0.360 AC4 0.570

BP3 0.727 MFP2 0.620

BC2 0.736 MFP3 0.593

BC3 0.713 MFP4 0.729

BRA1 0.398 BP_M 0.339

BRA2 0.441 BC_M 0.706

BRA3 0.703 BRA_M 0.496


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100

BRA4 0.557 AC_M 0.845

AC2 0.516 MFP_M 0.595

AC3 0.785 B_P 0.596


5.5 Structural Equation Model

SEM was employed to explain the relationships among the multiple variables for budgeting

practices and financial performance. The structural model in SEM describes the associations

among the latent constructs (Kline, 2012). It spells out how certain constructs directly or

indirectly influence the values of other constructs in the model (Bryne, 2013) and how those

constructs are associated with each other and are used for hypotheses testing. Figure 4 shows

the structural model for budgeting practices and financial performance.

Raykov et al., (1992) recommend that acceptable SEM models are typically associated with

chi-square values that are low for a given number of degrees of freedom, with matching p

values greater than the pre-set significance level, as well as with high descriptive goodness-of-

fit indexes (GFI, or NFI, NNFI, or CFI--depending on the program used) and a low root-mean-

square residual (when LISREL is used). Even though there is some vagueness as to which

descriptive index of fit under which circumstances is more instructive with respect to model

fit, no single descriptive index of fit appears to be better than the others and flawless in this

regard (Raykov et al., 1992). Consequently, in this study, we used Chi-square (χ2), CMIN/DF

(x2 /df, RMSEA, IFI, CFI, NFI, GFI, and AGFI for Structural Equation Model for Influence

of Budgeting Practices on Financial Performance of Manufacturing SME. The SEM represents

the graphical outlay of its mathematical expression, where there is an interrelation of the

dependent variables to their explanatory variables by a set of equations. The outputs, both

graphical and textual, are presented and discussed as follows.


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101

Chi-square (χ2) = 231.701, DF = 113, P-VALUE = 0.000, CMIN/DF (x2 /df =2.051,

RMSEA = 0.066, IFI= 0.944, CFI= 0.943, NFI= 0.896, GFI =0.897, AGFI=0.860

Figure 4: SEM Model for Budgeting Practices

Figure 4 shows that when Budgeting Practices increased by one SD, Financial performance

improved by 0.714 SD. Squared multiple correlations (R2) indicated that Budgeting Practices

accounted for 0.714 variances in financial performance. There were seven unobserved and 17

observed variables. The model was recursive with a sample size of 156. The R-Squared values

are shown in Table 13 for ease of readability and to avoid congesting Figure 4.


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102

Table 13: R- Square Values for Financial Performance as Dependent Variable

Variable R-square Variable R-square

BP2 0.361 MFP3 0.593

BP3 0.725 MFP4 0.731

BC2 0.727 FPM1 0.675

BC3 0.722 FPM2 0.832

BRA1 0.402 FPM3 0.636

BRA2 0.441 BP 0.346

BRA3 0.699 BC 0.691

BRA4 0.557 BRA 0.510

AC2 0.524 AC 0.846

AC3 0.780 MFP 0.596

AC4 0.568 B_P 0.188

MFP2 0.617 FPM 0.714

Table 14 gives the various measures of fit indices used for the influence of budgeting practices

on financial performance. The fit indices signified a perfect model fit as seen on the path indices

of the structural model: Chi-square (χ2) = 231.701, DF = 113, P-VALUE = 0.000, CMIN/DF

(x2 /df =2.051, RMSEA = 0.066, IFI= 0.944, CFI= 0.943, NFI= 0.896, GFI =0.897,

AGFI=0.860. The p-value was 0.000, hence, the conclusion drawn was that, the model fitted

the data perfectly well.

Table 14: Measures of fit of Influence of Budgeting Practices on Financial performance

Fit Measures

Parameter

Fit Measures

Indicators

Interpretation This Model

Results

Comment

Chi-square (χ2) <0.5

>0.5

Acceptable

Acceptable fit

231.701

CMIN/DF (x2

/df

<1

1 – 3

>3

Over fit

Good fit

Over fit

2.050 Good Fit

RMSEA 0<=

About 0.05<=

About 0.08<=

>0.1

Exact fit

Close fit

Reasonable fit

Over fit

0.066 Reasonable

IFI 0 –1

Close to 1

Fit

Very good fit

0.944 Very Good


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103

>1 Over fit

CFI 0 – 1

Close to 1

>1

Fit

Very good fit

Over fit

0.943 Very Good

NFI 0 – 1

Close to 1

>1

Fit

Very good fit

Over fit

0.896 Very Good

GFI 0 – 1

Close to 1

>1

Fit

Very good fit

Over fit

0.897 Very Good

AGFI 0 – 1

1

>1

Very good fit

Perfect fit

Over fit

0.860 Very Good

Fit


5.6 Hypothesis testing

The following section shows the direct research hypothesis testing that was conducted by

analyzing the path significance of each relationship. Table 15 shows the Path Coefficients for

Influence of Budgeting Practices on Financial performance. The structural equation model was

taken into account. All the paths reflect literature findings, and Figure 4 above shows the

graphical outlay of SEM. For objective one, which was to determine the influence of budgeting

practices on the financial performance of manufacturing SMEs in Nairobi County, Kenya. The

null hypothesis was stated as follows – H0: budgeting practices have no influence on the

financial performance of manufacturing SMEs in Nairobi County, Kenya.

Table 15: Path Coefficients for Influence of Budgeting Practices on Financial performance

LHS PATH RHS ESTIMATE STD ERROR Z-

SCORE

P-

VALUE

CI-LOWER CI-UPPER

FPM → B_P 0.434 0.062 7.050 0.000 0.313 0.555

B_P → BP 0.588 0.066 8.926 0.000 0.459 0.718

B_P → BC 0.831 0.036 23.290 0.000 0.761 0.901

B_P → BRA 0.714 0.045 15.799 0.000 0.625 0.803

B_P → AC 0.920 0.029 31.428 0.000 0.862 0.977

B_P → MFP 0.772 0.040 19.503 0.000 0.694 0.849


From the path analysis results displayed in Table 15, we draw the conclusions and output

presented in Table 16. All the five constructs and associated hypotheses were proved by the

findings of this study.


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104

Table 16: Hypothesis Testing Conclusion

Hypothesis p-value Comment

H1 Budgeting practices significantly influences

financial performance of manufacturing SMEs in

Nairobi City County 0.000 Proved

H1a Planning for Cash flows significantly influences

budgeting practices of manufacturing SMEs in


H1b Controlling Cash flows significantly influences



H1c Allocating Resources significantly influences



H1d Coordinating Activities significantly influences

budgeting practices of manufacturing SMEs in Proved

Nairobi City County 0.000

H1e. Monitoring Financial Position significantly influences

budgeting practices of manufacturing SMEs in Proved

Nairobi City County 0.000

Source: Researcher (2020

Consequently, from the results of Table 16, we reject the null hypothesis H0 Budgeting

practices have no influence on the financial performance of manufacturing SMEs in Nairobi

County, Kenya. We accept the alternative hypothesis:H1 Budgeting practices significantly

influence the financial performance of manufacturing SMEs in Nairobi City County. Further,

the structural equation model is as follows:

Budgeting practices (B_P) =Planning for Cash Flows (BP) + Budget Control (BC) +

Allocating Resources (BRA) +Activity Coordination (AC) +

Monitoring Financial Position (MFP) + Error term

= 0.588BP + 0.831AC + 0.714BRA + 0.920AC + 0.772MFP +

Error

Financial Performance (FPM)= Budgeting practices (B_P) + Error term

= 0.434B_P + Error

6. DISCUSSION

The alternative hypothesis six in this paper tested the relationship between Budgeting

Practices (B_P) and Financial Performance. These study findings confirm that there has

been a significant increase in the use of budgeting practices (BP) by the manufacturing

SMEs in Nairobi County, Kenya. The study found out that planning for Cash flows (BP),

Controlling Cash flows (BC), Resources Allocation (BRA), Activity Coordination (AC),

and Monitoring Financial Position (MFP) have all been implemented to a great extent

largely as part of Budgeting practices. This designates that the Nairobi County

Manufacturing SME firms in Kenya have deployed these five budgeting practices.


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105

The null hypothesis for this paper states that, “There is no significant relationship between

budgeting practices and manufacturing SMEs financial performance”. Since the structural

equation modelling values for the path analysis on Budgeting practices (B_P) and Financial

Performance (FPM) are β=0.434, p=0.000 which is less than the threshold value at 0.001

level of significance, the null hypothesis is rejected. We therefore accept the hypothesis

that “There is a significant positive relationship between budgeting practices and

manufacturing SMEs financial performance”

The study concluded that Budgeting practices have a strong (R-squared value=0.714)

influence on the financial performance of manufacturing SMEs in Nairobi City County.

The findings of the study agree with the findings of several preceding studies globally,

regionally, and locally. King, Clarkson, and Wallace (2010) conducted research to

determine the relationship between budgeting practices and firm performance in small

healthcare businesses in Australia, where the results support the proclamation that the

performance of a firm is linked to its choice of budgeting practices. Armitage, Webb, and

Glynn (2016) investigated the use of MA techniques by Canadian SMEs. Among the

methods investigated were budget reporting and analysis for decision-making. The results

of the study found most SMEs studied often used operational budgets such as master

budgets, quarterly and rolling budgets at highly sophisticated levels. In addition, the study

found that smaller companies focused more on the cash component of their operational

budgets and that as the SME size increased, the complexity of its operational budget also

increased. Mulani et al., (2015) examined the effects of the budgetary process on the

performance of SMEs in India and found out that the performance of SMEs in India is

affected by the characteristics of the budget goals. A study carried out in Sri Lanka

researched on the budgetary process and organizational performance of apparel industry

(Silva & Jayamaha, 2012). The study concluded that efficient apparel companies should

sustain sound budgetary processes for increased levels of organizational performance. The

findings suggested that companies in the industry intending to increase their financial

performance should improve their budgetary processes. Abdullah et al., (2015) examined

the role of budgetary control on the performance of Tahir Guest House, Kano State in

Nigeria. They found out those budgetary factors such as target budget setting, budget

administration, and budget process played a significant role in influencing the firm's

performance.

Locally, Isaboke and Kwasira, (2016) conducted a study to determine the influence of

budgeting process on the financial performance of the County Government of Nakuru and

established that the budgeting process strongly influenced the county government's

financial performance. Kimunguyi et al., (2015) evaluated the way budgetary process

affected the financial performance of NGOs in health sector in Kenya and found out that

budgetary management practices had a positive effect on the NGOs' financial performance

in Kenya.

7. CONCLUSIONS AND RECOMMENDATIONS

This study has highlighted the importance of the budgeting practices measured by four

constructs, namely Planning for Cash flows (BP), Controlling Cash flows (BC), Resources

Allocation (BRA), Activity Coordination (AC), and Monitoring Financial Position (MFP) and

their influence on the financial performance of manufacturing SMEs. Since the said influence

is positive and significant, it implies that there is a great need for manufacturing SMEs to


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106

deploy budget accounting practices. To make this feasible and achievable, policy

implementers, sector players, the government of Kenya, accounting bodies, and financial

institutions should develop proper policies and regulations. The adoption of the budgeting

practices will enable manufacturing SMEs to improve their financial performance and hence

effectively contribute to the national economy in both wealth and job creation in Kenya. The

study concluded that the measures of budgeting practices, namely Planning for Cash flows

(BP), Controlling Cash flows (BC), Resources Allocation (BRA), Activity Coordination (AC),

and Monitoring Financial Position (MFP) explained 59.6 % of the variations in budgeting

practices in manufacturing SMEs. Moreover, overall budgeting practices explained 71.4% of

the financial performance of SMEs.

This research established that budgeting practices are used in the manufacturing of small

and medium enterprises in Nairobi City County, Kenya. It also revealed that the firms

indicated the existence of established budgeting practices. This is an indicator that the firm's

Planning for Cash flows (BP), Controlling Cash flows (BC), Resources Allocation (BRA),

Activity Coordination (AC), and Monitoring Financial Position (MFP) are well-established

practices. Moreover, they influence financial performance. Therefore, manufacturing SME

management is guided to pay more focus to the budgeting practices since it improves their

firm performance.

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